sits_segment {sits}

Segment an image

Description

Apply a spatial-temporal segmentation on a data cube based on a user defined segmentation function. The function applies the segmentation algorithm "seg_fn" to each tile.

Segmentation uses the following steps:

1. Create a regular data cube with sits_cube and sits_regularize;

2. Run sits_segment to obtain a vector data cube with polygons that define the boundary of the segments;

3. Classify the time series associated to the segments with sits_classify, to get obtain a vector probability cube;

4. Use sits_label_classification to label the vector probability cube;

5. Display the results with plot or sits_view.

Usage

sits_segment(
  cube,
  seg_fn = sits_slic(),
  roi = NULL,
  impute_fn = impute_linear(),
  start_date = NULL,
  end_date = NULL,
  memsize = 8,
  multicores = 2,
  output_dir,
  version = "v1",
  progress = TRUE
)

Arguments

cube

Regular data cube

seg_fn

Function to apply the segmentation

roi

Region of interest (see below)

impute_fn

Imputation function to remove NA values.

start_date

Start date for the segmentation

end_date

End date for the segmentation.

memsize

Memory available for classification (in GB).

multicores

Number of cores to be used for classification.

output_dir

Directory for output file.

version

Version of the output (for multiple segmentations).

progress

Show progress bar?

Value

A tibble of class 'segs_cube' representing the segmentation.

Note

The "roi" parameter defines a region of interest. It can be an sf_object, a shapefile, or a bounding box vector with named XY values ("xmin", "xmax", "ymin", "ymax") or named lat/long values ("lon_min", "lat_min", "lon_max", "lat_max")

Author(s)

Gilberto Camara, [e-mail]

Rolf Simoes, [e-mail]

Felipe Carvalho, [e-mail]

Examples

if (sits_run_examples()) {
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    # create a data cube
    cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6",
        data_dir = data_dir
    )
    # segment the vector cube
    segments <- sits_segment(
        cube = cube,
        output_dir = tempdir()
    )
    # create a classification model
    rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
    # classify the segments
    seg_probs <- sits_classify(
        data = segments,
        ml_model = rfor_model,
        output_dir = tempdir()
    )
    # label the probability segments
    seg_label <- sits_label_classification(
        cube = seg_probs,
        output_dir = tempdir()
    )
}